Zero-Shot Classification
Transformers
Safetensors
English
matilda_jev
feature-extraction
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/Matilda-Jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Maincode/Matilda-Jev-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/Matilda-Jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download configuration_matilda_jev.py from Maincode/Matilda-Jev-v1: direct link, hf CLI and curl.
- Browser
- Download file 603 Bytes
-
https://huggingface.co/Maincode/Matilda-Jev-v1/resolve/main/configuration_matilda_jev.py
- Command line
-
hf download hf://Maincode/Matilda-Jev-v1/configuration_matilda_jev.py
-
curl -L -o configuration_matilda_jev.py https://huggingface.co/Maincode/Matilda-Jev-v1/resolve/main/configuration_matilda_jev.py
603 Bytes
| """Maincode MATILDA configuration aliases for the Transformers backbone.""" | |
| from transformers.models.qwen3_5.configuration_qwen3_5 import ( | |
| Qwen3_5Config, Qwen3_5TextConfig, Qwen3_5VisionConfig, | |
| ) | |
| class MatildaJevTextConfig(Qwen3_5TextConfig): | |
| model_type = "matilda_jev_text" | |
| class MatildaJevVisionConfig(Qwen3_5VisionConfig): | |
| model_type = "matilda_jev_vision" | |
| class MatildaJevConfig(Qwen3_5Config): | |
| model_type = "matilda_jev" | |
| sub_configs = {"text_config": MatildaJevTextConfig, "vision_config": MatildaJevVisionConfig} | |
| MatildaJevConfig.register_for_auto_class("AutoConfig") | |